Multi-Objective Optimization Using Genetic Algorithms

Multi-objective optimization using genetic algorithms represents an optimization methodology developed within the MATLAB environment. Genetic algorithms are heuristic optimization techniques that simulate biological evolutionary mechanisms to solve complex multi-objective problems. Through MATLAB implementation, developers can leverage genetic algorithm advantages to optimize multiple objective functions simultaneously, obtaining Pareto-optimal solutions. Key implementation aspects include chromosome encoding of decision variables, fitness assignment using non-dominated sorting, and evolutionary operators like tournament selection, simulated binary crossover, and polynomial mutation. This approach significantly enhances problem-solving efficiency while discovering superior solutions for complex multi-objective optimization challenges. The MATLAB Global Optimization Toolbox provides essential functions such as gamultiobj for implementing NSGA-II (Non-dominated Sorting Genetic Algorithm II), enabling effective handling of conflicting objectives through population-based search mechanisms.